Zobrazeno 1 - 10
of 171
pro vyhledávání: '"Mota João F"'
This paper introduces a novel reconfigurable and power-efficient FPGA (Field-Programmable Gate Array) implementation of an operator splitting algorithm for Non-Terrestial Network's (NTN) relay satellites model predictive orientation control (MPC). Ou
Externí odkaz:
http://arxiv.org/abs/2406.00402
The joint source-channel coding (JSCC) framework leverages deep learning to learn from data the best codes for source and channel coding. When the output signal, rather than being binary, is directly mapped onto the IQ domain (complex-valued), we cal
Externí odkaz:
http://arxiv.org/abs/2312.14792
In this paper, we improve upon our previous work[24,22] and establish convergence bounds on the objective function values of approximate proximal-gradient descent (AxPGD), approximate accelerated proximal-gradient descent (AxAPGD) and approximate pro
Externí odkaz:
http://arxiv.org/abs/2306.16964
We propose Dual-Feedback Generalized Proximal Gradient Descent (DFGPGD) as a new, hardware-friendly, operator splitting algorithm. We then establish convergence guarantees under approximate computational errors and we derive theoretical criteria for
Externí odkaz:
http://arxiv.org/abs/2306.16935
With nearly one million new cases diagnosed worldwide in 2020, head \& neck cancer is a deadly and common malignity. There are challenges to decision making and treatment of such cancer, due to lesions in multiple locations and outcome variability be
Externí odkaz:
http://arxiv.org/abs/2304.08106
Retinal fundus images can be an invaluable diagnosis tool for screening epidemic diseases like hypertension or diabetes. And they become especially useful when the arterioles and venules they depict are clearly identified and annotated. However, manu
Externí odkaz:
http://arxiv.org/abs/2303.18022
Autor:
Mourya, Rahul, Mota, João F. C.
End-to-end deep neural networks (DNNs) have become the state-of-the-art (SOTA) for solving inverse problems. Despite their outstanding performance, during deployment, such networks are sensitive to minor variations in the testing pipeline and often f
Externí odkaz:
http://arxiv.org/abs/2211.03177
We analyse the convergence of an approximate, fully inexact, ADMM algorithm under additive, deterministic and probabilistic error models. We consider the generalized ADMM scheme that is derived from generalized Lagrangian penalty with additive (smoot
Externí odkaz:
http://arxiv.org/abs/2210.02094
We analyse the convergence of the proximal gradient algorithm for convex composite problems in the presence of gradient and proximal computational inaccuracies. We derive new tighter deterministic and probabilistic bounds that we use to verify a simu
Externí odkaz:
http://arxiv.org/abs/2203.02204
Autor:
Orsso, Camila E., Gormaz, Teresita, Valentine, Sabina, Trottier, Claire F., Matias de Sousa, Iasmin, Ferguson-Pell, Martin, Johnson, Steven T., Kirkham, Amy A., Klein, Douglas, Maeda, Nathanial, Mota, João F., Neil-Sztramko, Sarah E., Quintanilha, Maira, Salami, Bukola Oladunni, Prado, Carla M.
Publikováno v:
In Methods November 2024 231:45-54